FEMA National Risk Index
Data for Eastern Shore Region
Michele Claibourn
6/24/2021
Data Source
Source: FEMA, National Risk Index, October 2020 release.
The Data Used here
The National Risk Index is intended to provide a view of the natural hazard risk within communities. While FEMA includes information on 18 natural hazards, we focus on six – coastal flooding, drought, heat wave, hurricane, riverine flooding, and strong wind – pulling measures on
- frequency (measuring the number of events or event days during a reporting period and the estimated annualized frequency or probability),
- exposure (measuring the building value, people, or agricultural value exposed to the natural hazard event), and
- historic loss ratio (measuring the proportion of building value, people, or agricultural value that has been historically impacted by the natural hazard).
The NRI uses data on natural hazards from multiple sources and estimates natural hazard frequency, exposure, and historic loss at the census tract level.
To learn more, see:
Variable descriptions
glimpse(nri)## Rows: 13
## Columns: 76
## $ OID_ <dbl> 47837, 47838, 63468, 63661, 65308, 65326, 69691, 69695, 697…
## $ NRI_ID <chr> "T51001090600", "T51001980100", "T51131930100", "T511319302…
## $ STATE <chr> "Virginia", "Virginia", "Virginia", "Virginia", "Virginia",…
## $ STATEABBRV <chr> "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA",…
## $ STATEFIPS <dbl> 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51
## $ COUNTY <chr> "Accomack", "Accomack", "Northampton", "Northampton", "Nort…
## $ COUNTYTYPE <chr> "County", "County", "County", "County", "County", "County",…
## $ COUNTYFIPS <chr> "001", "001", "131", "131", "131", "001", "001", "001", "00…
## $ STCOFIPS <dbl> 51001, 51001, 51131, 51131, 51131, 51001, 51001, 51001, 510…
## $ TRACT <chr> "090600", "980100", "930100", "930200", "930300", "090300",…
## $ TRACTFIPS <dbl> 51001090600, 51001980100, 51131930100, 51131930200, 5113193…
## $ POPULATION <dbl> 4401, 0, 4376, 3820, 4193, 2335, 2941, 5, 6234, 4907, 2849,…
## $ BUILDVALUE <dbl> 665181000, 3772000, 595521000, 380188000, 603745000, 211228…
## $ AGRIVALUE <dbl> 14720233.70, 218489.79, 21407021.39, 43535471.38, 31048507.…
## $ AREA <dbl> 49.325259, 12.157470, 53.135749, 71.502115, 87.054823, 49.5…
## $ CFLD_EVNTS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ CFLD_AFREQ <dbl> 2.743370, 2.709987, 1.202226, 1.461385, 1.292462, 2.482070,…
## $ CFLD_EXPB <dbl> 367905008, 3772000, 110856599, 86004724, 310758713, 1348494…
## $ CFLD_EXPP <dbl> 2434.14941, 0.00000, 814.59508, 864.14628, 2158.21462, 1490…
## $ CFLD_EXPPE <dbl> 18012705627, 0, 6028003599, 6394682493, 15970788179, 110310…
## $ CFLD_EXPT <dbl> 18380610635, 3772000, 6138860198, 6480687217, 16281546892, …
## $ CFLD_HLRB <dbl> 0.001493404, 0.001493404, 0.003208678, 0.003208678, 0.00320…
## $ CFLD_HLRP <dbl> 3.011133e-07, 3.011133e-07, 3.011133e-07, 3.011133e-07, 3.0…
## $ CFLD_HLRR <chr> "Very Low", "Relatively High", "Relatively Low", "Relativel…
## $ DRGT_EVNTS <dbl> 91, 42, 28, 28, 28, 63, 42, 42, 42, 35, 42, 28, 42
## $ DRGT_AFREQ <dbl> 5.055556, 2.333333, 1.555556, 1.555556, 1.555556, 3.500000,…
## $ DRGT_EXPB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPPE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPA <dbl> 4848309, 0, 0, 43535471, 23747093, 0, 0, 0, 39002636, 30104…
## $ DRGT_EXPT <dbl> 4848309, 0, 0, 43535471, 23747093, 0, 0, 0, 39002636, 30104…
## $ DRGT_HLRB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_HLRP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_HLRA <dbl> 0.001584889, 0.001584889, 0.003693487, 0.003693487, 0.00369…
## $ DRGT_HLRR <chr> "Relatively Moderate", "No Rating", "No Rating", "Very High…
## $ HWAV_EVNTS <dbl> 6, 11, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 11
## $ HWAV_AFREQ <dbl> 0.4942339, 0.5766009, 0.5766063, 0.5766063, 0.5766063, 0.49…
## $ HWAV_EXPB <dbl> 665180718, 3772000, 595520944, 380187676, 603744461, 211227…
## $ HWAV_EXPP <dbl> 4400.999, 0.000, 4375.999, 3819.996, 4192.997, 2334.998, 29…
## $ HWAV_EXPPE <dbl> 32567391524, 0, 32382392576, 28267973910, 31028174325, 1727…
## $ HWAV_EXPT <dbl> 33232572241, 3772000, 32977913520, 28648161585, 31631918786…
## $ HWAV_HLRB <dbl> 3.60888e-10, 3.60888e-10, 2.97900e-12, 2.97900e-12, 2.97900…
## $ HWAV_HLRP <dbl> 2.901945e-07, 2.901945e-07, 2.901945e-07, 2.901945e-07, 2.9…
## $ HWAV_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ HRCN_EVNTS <dbl> 33, 21, 28, 22, 25, 26, 21, 21, 26, 24, 25, 26, 27
## $ HRCN_AFREQ <dbl> 0.1967287, 0.2214110, 0.2239624, 0.2238779, 0.2406999, 0.22…
## $ HRCN_EXPB <dbl> 664767958, 3769524, 595340857, 380080256, 603484965, 211227…
## $ HRCN_EXPP <dbl> 4398.720, 0.000, 4374.935, 3818.995, 4191.471, 2334.992, 29…
## $ HRCN_EXPPE <dbl> 32550526424, 0, 32374516646, 28260566446, 31016886195, 1727…
## $ HRCN_EXPT <dbl> 33215294382, 3769524, 32969857503, 28640646702, 31620371160…
## $ HRCN_HLRB <dbl> 0.0006539852, 0.0006539852, 0.0011742747, 0.0011742747, 0.0…
## $ HRCN_HLRP <dbl> 6.528385e-07, 6.528385e-07, 1.377019e-06, 1.377019e-06, 1.3…
## $ HRCN_HLRR <chr> "Very Low", "Relatively Moderate", "Very Low", "Very Low", …
## $ RFLD_EVNTS <dbl> 13, 13, 6, 6, 6, 13, 13, 13, 13, 13, 13, 13, 13
## $ RFLD_AFREQ <dbl> 0.5909091, 0.5909091, 0.2727273, 0.2727273, 0.2727273, 0.59…
## $ RFLD_EXPB <dbl> 202317003.4, 3429843.9, 46695447.8, 44570613.2, 149882632.4…
## $ RFLD_EXPP <dbl> 1408.94892, 0.00000, 276.46151, 329.02285, 763.49921, 919.0…
## $ RFLD_EXPPE <dbl> 10426222026, 0, 2045815190, 2434769122, 5649894147, 6800847…
## $ RFLD_EXPA <dbl> 3092313.16, 175094.85, 1135609.61, 2408639.57, 1343780.69, …
## $ RFLD_EXPT <dbl> 1.063163e+10, 3.604939e+06, 2.093646e+09, 2.481748e+09, 5.8…
## $ RFLD_HLRB <dbl> 0.0004153736, 0.0004153736, 0.0037380237, 0.0037380237, 0.0…
## $ RFLD_HLRP <dbl> 3.925184e-06, 3.925184e-06, 1.824847e-05, 1.824847e-05, 1.8…
## $ RFLD_HLRA <dbl> 0.001257540, 0.001257540, 0.008719085, 0.008719085, 0.00871…
## $ RFLD_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ SWND_EVNTS <dbl> 209, 161, 150, 146, 115, 162, 162, 162, 151, 155, 158, 142,…
## $ SWND_AFREQ <dbl> 6.533043, 5.062500, 4.710873, 4.582564, 3.599022, 5.076151,…
## $ SWND_EXPB <dbl> 665181000, 3772000, 595521000, 380188000, 603745000, 211228…
## $ SWND_EXPP <dbl> 4401, 0, 4376, 3820, 4193, 2335, 2941, 5, 6234, 4907, 2849,…
## $ SWND_EXPPE <dbl> 32567400000, 0, 32382400000, 28268000000, 31028200000, 1727…
## $ SWND_EXPA <dbl> 14720233.70, 218489.79, 21407021.39, 43535471.38, 31048507.…
## $ SWND_EXPT <dbl> 33247301234, 3990490, 32999328021, 28691723471, 31662993507…
## $ SWND_HLRB <dbl> 1.648158e-05, 1.648158e-05, 1.927672e-05, 1.927672e-05, 1.9…
## $ SWND_HLRP <dbl> 6.850051e-08, 6.850051e-08, 3.424598e-07, 3.424598e-07, 3.4…
## $ SWND_HLRA <dbl> 2.435583e-06, 2.435583e-06, 2.435583e-06, 2.435583e-06, 2.4…
## $ SWND_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ NRI_VER <chr> "October 2020", "October 2020", "October 2020", "October 20…Observations are census tract estimates of…
- Population, building value, agricultural value, and area within tract
- Natural hazards include: CFLD - coastal flooding, DRGT - drought, HWAV - heat wave, HRCN - hurricane, RFLD - riverine flooding, SWND - strong wind
- Hazard measures include: EVNTS - number of events in recording period, AFREQ - annualized frequency (# events/# years in recording period)
- Exposure measures include: EXPB - building value exposure, EXPP - population exposure, EXPE - population equivalence exposure, EXPA - agricultural value exposure
- Historic loss ratio measures include: HLRB - historic loss ratio for building value, HLRA - historicla loss ratio for agriculture, HLRP - historical loss ratio for population, HLRR - historic loss ratio overall
Summaries
5-number summaries of (non-missing) numeric variables (remove tract identifiers)
nri %>% select(-c(OID_:STATEFIPS, COUNTYTYPE:TRACTFIPS, NRI_VER)) %>%
select(where(~is.numeric(.x) && !is.na(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 0,
summary.stat = c("mean", "sd", "min", "median", "max"))##
## Summary Statistics
## ================================================================================
## Statistic Mean St. Dev. Min Median Max
## --------------------------------------------------------------------------------
## POPULATION 3,504 1,942 0 3,820 6,234
## BUILDVALUE 445,064,231 263,814,332 3,772,000 547,772,000 813,756,000
## AGRIVALUE 19,943,077 15,182,275 31,301 21,407,021 43,535,471
## AREA 51 27 7 53 87
## CFLD_AFREQ 2 1 1 2 3
## CFLD_EXPB 199,509,533 198,615,187 3,772,000 134,849,422 736,404,000
## CFLD_EXPP 1,402 936 0 1,452 2,941
## CFLD_EXPPE 10,371,633,091 6,922,727,785 0 10,746,133,053 21,763,400,000
## CFLD_EXPT 10,571,142,624 7,099,434,508 3,772,000 10,926,364,382 22,499,804,000
## CFLD_HLRB 0 0 0 0 0
## CFLD_HLRP 0 0 0 0 0
## DRGT_EVNTS 43 17 28 42 91
## DRGT_AFREQ 2 1 2 2 5
## DRGT_EXPA 12,767,239 16,840,163 0 0 43,535,471
## DRGT_EXPT 12,767,239 16,840,163 0 0 43,535,471
## DRGT_HLRA 0 0 0 0 0
## HWAV_EVNTS 7 2 6 6 11
## HWAV_AFREQ 1 0 0 0 1
## HWAV_EXPB 445,064,081 263,814,292 3,772,000 547,771,848 813,755,973
## HWAV_EXPP 3,504 1,942 0 3,820 6,234
## HWAV_EXPPE 25,930,157,957 14,370,703,000 0 28,267,973,910 46,131,584,530
## HWAV_EXPT 26,375,222,038 14,594,282,423 3,772,000 28,648,161,585 46,679,356,378
## HWAV_HLRB 0 0 0 0 0
## HWAV_HLRP 0 0 0 0 0
## HRCN_EVNTS 25 3 21 25 33
## HRCN_AFREQ 0 0 0 0 0
## HRCN_EXPB 444,801,528 263,624,465 3,769,524 547,726,825 813,598,176
## HRCN_EXPP 3,503 1,942 0 3,819 6,234
## HRCN_EXPPE 25,920,530,241 14,369,165,198 0 28,260,566,446 46,129,744,254
## HRCN_EXPT 26,365,331,769 14,592,649,165 3,769,524 28,640,646,702 46,677,471,079
## HRCN_HLRB 0 0 0 0 0
## HRCN_HLRP 0 0 0 0 0
## RFLD_EVNTS 11 3 6 13 13
## RFLD_AFREQ 1 0 0 1 1
## RFLD_EXPB 106,469,319 161,601,174 301,320 51,513,125 608,324,065
## RFLD_EXPP 605 662 0 340 2,374
## RFLD_EXPPE 4,479,443,695 4,897,974,424 0 2,517,678,037 17,566,176,693
## RFLD_EXPA 1,935,370 1,612,333 26,074 1,964,586 5,149,665
## RFLD_EXPT 4,587,848,385 5,052,492,472 364,660 2,574,672,609 18,174,526,832
## RFLD_HLRB 0 0 0 0 0
## RFLD_HLRP 0 0 0 0 0
## RFLD_HLRA 0 0 0 0 0
## SWND_EVNTS 156 20 115 158 209
## SWND_AFREQ 5 1 4 5 7
## SWND_EXPB 445,064,231 263,814,332 3,772,000 547,772,000 813,756,000
## SWND_EXPP 3,504 1,942 0 3,820 6,234
## SWND_EXPPE 25,930,169,231 14,370,706,471 0 28,268,000,000 46,131,600,000
## SWND_EXPA 19,943,077 15,182,275 31,301 21,407,021 43,535,471
## SWND_EXPT 26,395,176,538 14,606,328,047 3,990,490 28,691,723,471 46,718,374,636
## SWND_HLRB 0 0 0 0 0
## SWND_HLRP 0 0 0 0 0
## SWND_HLRA 0 0 0 0 0
## --------------------------------------------------------------------------------Summaries of (non-missing) character variables (remove tract identifiers)
nri %>% select(-c(OID_:STATEFIPS, COUNTYTYPE:TRACTFIPS, NRI_VER)) %>%
select(where (~is.character(.x))) %>% map(tabyl)## $COUNTY
## .x[[i]] n percent
## Accomack 10 0.7692308
## Northampton 3 0.2307692
##
## $CFLD_HLRR
## .x[[i]] n percent
## Relatively High 1 0.07692308
## Relatively Low 4 0.30769231
## Relatively Moderate 1 0.07692308
## Very Low 7 0.53846154
##
## $DRGT_HLRR
## .x[[i]] n percent
## No Rating 7 0.5384615
## Relatively Moderate 4 0.3076923
## Very High 2 0.1538462
##
## $HWAV_HLRR
## .x[[i]] n percent
## Very Low 13 1
##
## $HRCN_HLRR
## .x[[i]] n percent
## Relatively Low 1 0.07692308
## Relatively Moderate 1 0.07692308
## Very Low 11 0.84615385
##
## $RFLD_HLRR
## .x[[i]] n percent
## Very Low 13 1
##
## $SWND_HLRR
## .x[[i]] n percent
## Very Low 13 1Visual distribution
Frequency distribution across tracts:
Tract assets
nri %>% select(TRACTFIPS:AREA) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
geom_histogram() +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% c("POPULATION", "BUILDVALUE", "AGRIVALUE")) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “POPULATION: Population exposure is defined as the estimated number of people to be exposed to a hazard. The maximum possible population exposure of an area is its population as recorded in Hazus 4.2 SP1 (https://msc.fema.gov/portal/resources/hazus).”
[2] “BUILDVALUE: Building exposure is defined as the dollar value of the buildings exposed to a hazard. The maximum possible building exposure of an area is its building value as recorded in Hazus 4.2 (https://msc.fema.gov/portal/resources/hazus).”
[3] “AGRIVALUE: Agriculture exposure is defined as the estimated dollar value of the crops and livestock exposed to a hazard. This is derived from the USDA 2017 Census of Agriculture county-level value of crop and pastureland (https://www.nass.usda.gov/Publications/AgCensus/2017/index.php).”
Tract hazards: Coastal Flooding
# Tract hazards: CFLD
vars <- nri %>% select(contains("CFLD"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “CFLD_AFREQ: Coastal Flooding is when water inundates or covers normally dry coastal land as a result of high or rising tides or storm surges. Coastal Flooding frequency calculation is based on the model of the 1\% annual chance floodplain ()rather than historical flood events).” [2] “CFLD_EXPB: Based on the intersection of the Coastal Flooding polygon and each area, multiplied by the area’s building value density.”
[3] “CFLD_EXPP: Based on the intersectionof the Coastal Flooding polygon and each area, multiplied by the area’s population density.”
[4] “CFLD_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[5] “CFLD_EXPT: An aggregation of population exposure and building exposure to a hazard.”
[6] “CFLD_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure that experiences loss due to a Coastal Flooding event, or the average rate of loss associated with the occurrence of a Coastal Flooding event.”
[7] “CFLD_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure that experiences loss due to a Coastal Flooding event, or the average rate of loss associated with the occurrence of a Coastal Flooding event.”
Tract hazards: Drought
vars <- nri %>% select(contains("DRGT"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “DRGT_EVNTS: A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The number of Drought events are the number of days from 2000-2017 in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).”
[2] “DRGT_AFREQ: A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The annualized freqeuncy of Drought events are the average number of days per year (based on historical droughts from 2000-2017 ) in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).” [3] “DRGT_EXPA: Based on the intersection of the Drought polygon and each area, multipled by the area’s total agricultural value density.”
[4] “DRGT_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Drought event-day, or the average rate of loss associated with the occurrence of a Drought event-day.”
Tract hazards: Heat Wave
vars <- nri %>% select(contains("HWAV"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “HWAV_EVNTS: A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The number of Heat Wave event-days are the number of days from 2005-2017 in which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).”
[2] “HWAV_AFREQ: A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The annualized frequency of Heat Wave events are the average number of days per year (based on historical events from 2005-2017) for which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).” [3] “HWAV_EXPB: Based on the intersection of the Heat Wave region and each area, multipled by the area’s building value density.”
[4] “HWAV_EXPP: Based on the intersection of the Heat Wave region and each area, multipled by the area’s population density.”
[5] “HWAV_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “HWAV_EXPT: An aggregation of population exposure and building exposure to a hazard.”
[7] “HWAV_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Heat Wave event-day, or the average rate of loss associated with the occurrence of a Heat Wave event-day.”
[8] “HWAV_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Heat Wave event-day, or the average rate of loss associated with the occurrence of a Heat Wave event-day.”
Tract hazards: Hurricane
vars <- nri %>% select(contains("HRCN"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “HRCN_EVNTS: A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The number of events are the number of events occuring between 1851 and 2017 as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).”
[2] “HRCN_AFREQ: A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The annualized frequency of Hurricane events are the average number of events per year (based on historical events from 1851 and 2017) experienced by an area as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).” [3] “HRCN_EXPB: Based on the intersection of the buffered Huricane pahts and each area, multipled by the area’s building value density.”
[4] “HRCN_EXPP: Based on the intersection of the buffered Huricane pahts and each area, multipled by the area’s population density.”
[5] “HRCN_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “HRCN_EXPT: An aggregation of population exposure and building exposure to a hazard.”
[7] “HRCN_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Hurrican event, or the average rate of loss associated with the occurrence of a Hurricane event.”
[8] “HRCN_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Hurrican event, or the average rate of loss associated with the occurrence of a Hurricane event.”
Tract hazards: Riverine Flooding
vars <- nri %>% select(contains("RFLD"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “RFLD_EVNTS: Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The number of events are the number of events occuring between 1995 and 2016 in an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).”
[2] “RFLD_AFREQ: Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The annualized freqeuncy of Riverine Flooding events are the average number of events per year (based on historical events from 1995 and 2016) experienced by an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).” [3] “RFLD_EXPB: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s building value density.”
[4] “RFLD_EXPP: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s population density.”
[5] “RFLD_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “RFLD_EXPA: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s agricultural value density.”
[7] “RFLD_EXPT: An aggregation of population exposure, building exposure, and agricultural exposure to a hazard.”
[8] “RFLD_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
[9] “RFLD_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
[10] “RFLD_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
Tract hazards: Strong Wind
vars <- nri %>% select(contains("SWND"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “SWND_EVNTS: Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The number of Strong Wind event-days are the number of days from 1986-2017 in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).”
[2] “SWND_AFREQ: Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The annualized frequency of Strong Wind event-days are the average number of days per year (based on historical events) from 1986-2017) in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).” [3] “SWND_EXPB: Because Strong Wind can occur anywhere, the entire building value of an area is considered exposed to Strong Wind.”
[4] “SWND_EXPP: Because Strong Wind can occur anywhere, the entire population value of an area is considered exposed to Strong Wind.”
[5] “SWND_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “SWND_EXPA: Because Strong Wind can occur anywhere, the entire agricultural value of an area is considered exposed to Strong Wind.”
[7] “SWND_EXPT: An aggregation of population exposure, building exposure, and agricultural exposure to a hazard.”
[8] “SWND_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
[9] “SWND_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
[10] “SWND_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
Maps
Variation across tracts
Coastal Flooding
# CFLD
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$CFLD_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(CFLD_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$CFLD_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$CFLD_AFREQ,
title = "Coastal Flooding-#/year", opacity = 0.7)meta %>%
filter(varname == "CFLD_AFREQ") %>%
select(about) %>%
as.list()$about [1] “Coastal Flooding is when water inundates or covers normally dry coastal land as a result of high or rising tides or storm surges. Coastal Flooding frequency calculation is based on the model of the 1\% annual chance floodplain ()rather than historical flood events).”
Droughts
# DRGT
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$DRGT_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(DRGT_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$DRGT_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$DRGT_AFREQ,
title = "Drought-#/year", opacity = 0.7)meta %>%
filter(varname == "DRGT_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The annualized freqeuncy of Drought events are the average number of days per year (based on historical droughts from 2000-2017 ) in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).”
Heat Wave
# HWAV
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$HWAV_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(HWAV_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$HWAV_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$HWAV_AFREQ,
title = "Heat Wave-#/year", opacity = 0.7)meta %>%
filter(varname == "HWAV_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The annualized frequency of Heat Wave events are the average number of days per year (based on historical events from 2005-2017) for which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).”
Hurricane
# HRCN
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$HRCN_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(HRCN_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$HRCN_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$HRCN_AFREQ,
title = "Hurricane-#/year", opacity = 0.7)meta %>%
filter(varname == "HRCN_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The annualized frequency of Hurricane events are the average number of events per year (based on historical events from 1851 and 2017) experienced by an area as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).”
Riverine Flooding
# RFLD
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$RFLD_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(RFLD_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$RFLD_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$RFLD_AFREQ,
title = "Riverine Flooding-#/year", opacity = 0.7)meta %>%
filter(varname == "RFLD_AFREQ") %>%
select(about) %>%
as.list()$about [1] “Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The annualized freqeuncy of Riverine Flooding events are the average number of events per year (based on historical events from 1995 and 2016) experienced by an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).”
Strong Wind
# SWND
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$SWND_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = eastern_nri,
fillColor = ~pal(SWND_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
"Ann. Freq.: ", round(eastern_nri$SWND_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = eastern_nri$SWND_AFREQ,
title = "Strong Wind-#/year", opacity = 0.7)meta %>%
filter(varname == "SWND_AFREQ") %>%
select(about) %>%
as.list()$about [1] “Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The annualized frequency of Strong Wind event-days are the average number of days per year (based on historical events) from 1986-2017) in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).”
Nota Bene
- Several hazard rates are dominated by regional measures, with little variation identified within the region.
- Consider removing tracts 9901 and 9902 (covering only water) before analysis.